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[P] GLIP + SAM for zero-shot instance segmentation
GLIP (https://github.com/microsoft/GLIP), which i feel has flown under the radar, is capable of zero-shot object detection. i threw together a notebook that pairs it with the recently released Segment Anything Model (https://github.com/facebookresearch/segment-anything) to do zero-shot instance segmentation: https://colab.research.google.com/drive/1kfdizAJiD5_t-M6yFBB6t2vzGrYg8SJc
submitted by /u/esmooth
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( 43
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[D] What's up with the sudden change to YouTube Transcripts? They seem much improved.
For years, while still useful, YouTube transcripts have been pretty terrible. No punctuation, poor translations of heavy accents, and generally difficult to comprehend.
Lo and behold today, I watch a video today from community favourite Károly Zsolnai-Fehér. You know, the Two Minute Papers guy, and..... hold onto your papers, the transcript is almost flawless. Fully punctuated and pretty flawless, even with his heavy English accent.
But I can't see any press about this? When did they transition to a new speech to text model? What model is it using? Anyone have any insight? Here is the video in question if anyone else is interested. https://www.youtube.com/watch?v=1KQc6zHOmtU
submitted by /u/Wooraah
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( 45
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[P] AI-Genie, type a project name and let GPT4 do the rest!
A small project I did a while ago.
Based on a prompt, I ask gpt4 to imagine the project name, architecture and the tools it will use.
I then ask it to implement each file in the project.
Most of the time the project wont run but it's a nice starting point.
Here is the github page: https://github.com/MrNothing/AI-Genie
Note: if your asked for a complex project, if can take a lot of API queries, you have been warned!
Thank you!
submitted by /u/smilefr
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( 43
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"Ending an Ugly Chapter in Chip Design: Study tries to settle a bitter disagreement over Google’s chip design AI" (independent replication of RL chip design work shows competitive with other approaches despite omitting pretraining)
submitted by /u/gwern
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( 42
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Information, Artificial Intelligence & the Problem of Scale
submitted by /u/UnicornyOnTheCob
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( 42
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"As an AI language model..."
submitted by /u/DavstrOne
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( 44
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The future of technology
submitted by /u/TheFootCrew_TFC
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( 42
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This AI app creates entire itineraries with recommendations for your next trip.
submitted by /u/rowancheung
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( 44
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Import data from over 40 data sources for no-code machine learning with Amazon SageMaker Canvas
Data is at the heart of machine learning (ML). Including relevant data to comprehensively represent your business problem ensures that you effectively capture trends and relationships so that you can derive the insights needed to drive business decisions. With Amazon SageMaker Canvas, you can now import data from over 40 data sources to be used […]
( 8
min )
Predicting new and existing product sales in semiconductors using Amazon Forecast
This is a joint post by NXP SEMICONDUCTORS N.V. & AWS Machine Learning Solutions Lab (MLSL) Machine learning (ML) is being used across a wide range of industries to extract actionable insights from data to streamline processes and improve revenue generation. In this post, we demonstrate how NXP, an industry leader in the semiconductor sector, […]
( 14
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Gaming on the Go: GeForce NOW Gives Members More Ways to Play
This GFN Thursday explores the many ways GeForce NOW members can play their favorite PC games across the devices they know and love. Plus, seven new games join the GeForce NOW library this week. More Ways to Play GeForce NOW is the ultimate platform for gamers who want to play across more devices than their Read article >
( 5
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Towards Self-Explainability of Deep Neural Networks with Heatmap Captioning and Large-Language Models. (arXiv:2304.02202v1 [cs.CV])
Heatmaps are widely used to interpret deep neural networks, particularly for
computer vision tasks, and the heatmap-based explainable AI (XAI) techniques
are a well-researched topic. However, most studies concentrate on enhancing the
quality of the generated heatmap or discovering alternate heatmap generation
techniques, and little effort has been devoted to making heatmap-based XAI
automatic, interactive, scalable, and accessible. To address this gap, we
propose a framework that includes two modules: (1) context modelling and (2)
reasoning. We proposed a template-based image captioning approach for context
modelling to create text-based contextual information from the heatmap and
input data. The reasoning module leverages a large language model to provide
explanations in combination with specialised knowledge. Our qualitative
experiments demonstrate the effectiveness of our framework and heatmap
captioning approach. The code for the proposed template-based heatmap
captioning approach will be publicly available.
( 2
min )
TM-vector: A Novel Forecasting Approach for Market stock movement with a Rich Representation of Twitter and Market data. (arXiv:2304.02094v1 [q-fin.ST])
Stock market forecasting has been a challenging part for many analysts and
researchers. Trend analysis, statistical techniques, and movement indicators
have traditionally been used to predict stock price movements, but text
extraction has emerged as a promising method in recent years. The use of neural
networks, especially recurrent neural networks, is abundant in the literature.
In most studies, the impact of different users was considered equal or ignored,
whereas users can have other effects. In the current study, we will introduce
TM-vector and then use this vector to train an IndRNN and ultimately model the
market users' behaviour. In the proposed model, TM-vector is simultaneously
trained with both the extracted Twitter features and market information.
Various factors have been used for the effectiveness of the proposed
forecasting approach, including the characteristics of each individual user,
their impact on each other, and their impact on the market, to predict market
direction more accurately. Dow Jones 30 index has been used in current work.
The accuracy obtained for predicting daily stock changes of Apple is based on
various models, closed to over 95\% and for the other stocks is significant.
Our results indicate the effectiveness of TM-vector in predicting stock market
direction.
( 3
min )
ConvFormer: Parameter Reduction in Transformer Models for 3D Human Pose Estimation by Leveraging Dynamic Multi-Headed Convolutional Attention. (arXiv:2304.02147v1 [cs.CV])
Recently, fully-transformer architectures have replaced the defacto
convolutional architecture for the 3D human pose estimation task. In this paper
we propose \textbf{\textit{ConvFormer}}, a novel convolutional transformer that
leverages a new \textbf{\textit{dynamic multi-headed convolutional
self-attention}} mechanism for monocular 3D human pose estimation. We designed
a spatial and temporal convolutional transformer to comprehensively model human
joint relations within individual frames and globally across the motion
sequence. Moreover, we introduce a novel notion of \textbf{\textit{temporal
joints profile}} for our temporal ConvFormer that fuses complete temporal
information immediately for a local neighborhood of joint features. We have
quantitatively and qualitatively validated our method on three common benchmark
datasets: Human3.6M, MPI-INF-3DHP, and HumanEva. Extensive experiments have
been conducted to identify the optimal hyper-parameter set. These experiments
demonstrated that we achieved a \textbf{significant parameter reduction
relative to prior transformer models} while attaining State-of-the-Art (SOTA)
or near SOTA on all three datasets. Additionally, we achieved SOTA for Protocol
III on H36M for both GT and CPN detection inputs. Finally, we obtained SOTA on
all three metrics for the MPI-INF-3DHP dataset and for all three subjects on
HumanEva under Protocol II.
( 2
min )
Deep Learning for Automated Experimentation in Scanning Transmission Electron Microscopy. (arXiv:2304.02048v1 [cond-mat.mtrl-sci])
Machine learning (ML) has become critical for post-acquisition data analysis
in (scanning) transmission electron microscopy, (S)TEM, imaging and
spectroscopy. An emerging trend is the transition to real-time analysis and
closed-loop microscope operation. The effective use of ML in electron
microscopy now requires the development of strategies for microscopy-centered
experiment workflow design and optimization. Here, we discuss the associated
challenges with the transition to active ML, including sequential data analysis
and out-of-distribution drift effects, the requirements for the edge operation,
local and cloud data storage, and theory in the loop operations. Specifically,
we discuss the relative contributions of human scientists and ML agents in the
ideation, orchestration, and execution of experimental workflows and the need
to develop universal hyper languages that can apply across multiple platforms.
These considerations will collectively inform the operationalization of ML in
next-generation experimentation.
( 2
min )
JPEG Compressed Images Can Bypass Protections Against AI Editing. (arXiv:2304.02234v1 [cs.LG])
Recently developed text-to-image diffusion models make it easy to edit or
create high-quality images. Their ease of use has raised concerns about the
potential for malicious editing or deepfake creation. Imperceptible
perturbations have been proposed as a means of protecting images from malicious
editing by preventing diffusion models from generating realistic images.
However, we find that the aforementioned perturbations are not robust to JPEG
compression, which poses a major weakness because of the common usage and
availability of JPEG. We discuss the importance of robustness for additive
imperceptible perturbations and encourage alternative approaches to protect
images against editing.
( 2
min )
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. (arXiv:2304.02064v1 [cs.LG])
We use information-theoretic tools to derive a novel analysis of Multi-source
Domain Adaptation (MDA) from the representation learning perspective.
Concretely, we study joint distribution alignment for supervised MDA with few
target labels and unsupervised MDA with pseudo labels, where the latter is
relatively hard and less commonly studied. We further provide
algorithm-dependent generalization bounds for these two settings, where the
generalization is characterized by the mutual information between the
parameters and the data. Then we propose a novel deep MDA algorithm, implicitly
addressing the target shift through joint alignment. Finally, the mutual
information bounds are extended to this algorithm providing a non-vacuous
gradient-norm estimation. The proposed algorithm has comparable performance to
the state-of-the-art on target-shifted MDA benchmark with improved memory
efficiency.
( 2
min )
Effective Theory of Transformers at Initialization. (arXiv:2304.02034v1 [cs.LG])
We perform an effective-theory analysis of forward-backward signal
propagation in wide and deep Transformers, i.e., residual neural networks with
multi-head self-attention blocks and multilayer perceptron blocks. This
analysis suggests particular width scalings of initialization and training
hyperparameters for these models. We then take up such suggestions, training
Vision and Language Transformers in practical setups.
( 2
min )
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A Characterization of Multioutput Learnability. (arXiv:2301.02729v4 [cs.LG] UPDATED)
We consider the problem of learning multioutput function classes in batch and
online settings. In both settings, we show that a multioutput function class is
learnable if and only if each single-output restriction of the function class
is learnable. This provides a complete characterization of the learnability of
multilabel classification and multioutput regression in both batch and online
settings. As an extension, we also consider multilabel learnability in the
bandit feedback setting and show a similar characterization as in the
full-feedback setting.
( 2
min )
Algorithm-Dependent Bounds for Representation Learning of Multi-Source Domain Adaptation. (arXiv:2304.02064v1 [cs.LG])
We use information-theoretic tools to derive a novel analysis of Multi-source
Domain Adaptation (MDA) from the representation learning perspective.
Concretely, we study joint distribution alignment for supervised MDA with few
target labels and unsupervised MDA with pseudo labels, where the latter is
relatively hard and less commonly studied. We further provide
algorithm-dependent generalization bounds for these two settings, where the
generalization is characterized by the mutual information between the
parameters and the data. Then we propose a novel deep MDA algorithm, implicitly
addressing the target shift through joint alignment. Finally, the mutual
information bounds are extended to this algorithm providing a non-vacuous
gradient-norm estimation. The proposed algorithm has comparable performance to
the state-of-the-art on target-shifted MDA benchmark with improved memory
efficiency.
( 2
min )
On the universal approximation property of radial basis function neural networks. (arXiv:2304.02220v1 [cs.LG])
In this paper we consider a new class of RBF (Radial Basis Function) neural
networks, in which smoothing factors are replaced with shifts. We prove under
certain conditions on the activation function that these networks are capable
of approximating any continuous multivariate function on any compact subset of
the $d$-dimensional Euclidean space. For RBF networks with finitely many fixed
centroids we describe conditions guaranteeing approximation with arbitrary
precision.
( 2
min )
Sequential Linearithmic Time Optimal Unimodal Fitting When Minimizing Univariate Linear Losses. (arXiv:2304.02141v1 [cs.LG])
This paper focuses on optimal unimodal transformation of the score outputs of
a univariate learning model under linear loss functions. We demonstrate that
the optimal mapping between score values and the target region is a rectangular
function. To produce this optimal rectangular fit for the observed samples, we
propose a sequential approach that can its estimation with each incoming new
sample. Our approach has logarithmic time complexity per iteration and is
optimally efficient.
( 2
min )
A relaxed proximal gradient descent algorithm for convergent plug-and-play with proximal denoiser. (arXiv:2301.13731v2 [stat.ML] UPDATED)
This paper presents a new convergent Plug-and-Play (PnP) algorithm. PnP
methods are efficient iterative algorithms for solving image inverse problems
formulated as the minimization of the sum of a data-fidelity term and a
regularization term. PnP methods perform regularization by plugging a
pre-trained denoiser in a proximal algorithm, such as Proximal Gradient Descent
(PGD). To ensure convergence of PnP schemes, many works study specific
parametrizations of deep denoisers. However, existing results require either
unverifiable or suboptimal hypotheses on the denoiser, or assume restrictive
conditions on the parameters of the inverse problem. Observing that these
limitations can be due to the proximal algorithm in use, we study a relaxed
version of the PGD algorithm for minimizing the sum of a convex function and a
weakly convex one. When plugged with a relaxed proximal denoiser, we show that
the proposed PnP-$\alpha$PGD algorithm converges for a wider range of
regularization parameters, thus allowing more accurate image restoration.
( 2
min )
Effective Theory of Transformers at Initialization. (arXiv:2304.02034v1 [cs.LG])
We perform an effective-theory analysis of forward-backward signal
propagation in wide and deep Transformers, i.e., residual neural networks with
multi-head self-attention blocks and multilayer perceptron blocks. This
analysis suggests particular width scalings of initialization and training
hyperparameters for these models. We then take up such suggestions, training
Vision and Language Transformers in practical setups.
( 2
min )
Generalisation under gradient descent via deterministic PAC-Bayes. (arXiv:2209.02525v3 [stat.ML] UPDATED)
We establish disintegrated PAC-Bayesian generalisation bounds for models
trained with gradient descent methods or continuous gradient flows. Contrary to
standard practice in the PAC-Bayesian setting, our result applies to
optimisation algorithms that are deterministic, without requiring any
de-randomisation step. Our bounds are fully computable, depending on the
density of the initial distribution and the Hessian of the training objective
over the trajectory. We show that our framework can be applied to a variety of
iterative optimisation algorithms, including stochastic gradient descent (SGD),
momentum-based schemes, and damped Hamiltonian dynamics.
( 2
min )
